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Comparison

eval-view vs just-eval

Verdict

Pick eval-view if eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions through diffing tool and; pick just-eval if just-eval provides a straightforward GPT-based framework for evaluating LLMs across multiple dimensions with clear outputs.

Markdown twin · eval-view alternatives · just-eval alternatives

GraphCanon updated Sep 20, 2026

eval-view logo

eval-view

hidai25/eval-view

★ 134pushed Sep 5, 2026
vs
just-eval logo

just-eval

Re-Align/just-eval

★ 90pushed Jan 29, 2024

Trust & integrity

Signaleval-viewjust-eval
Maintenance
Active (13d since push)
As of Sep 18, 2026 · github_public_v1
Dormant (953d since push)
As of Sep 9, 2026 · github_public_v1
Provenance
Not a fork · Personal account
As of Sep 18, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 9, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Sep 18, 2026 · osv@v1
No lockfile (source not queried)
As of Jul 15, 2026 · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
eval-view trust reportjust-eval trust reportTrust methodology

Tagline

eval-view
Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI
just-eval
A simple GPT-based evaluation tool for multi-aspect, interpretable assessment of LLMs.

Stars

eval-view
134
just-eval
90

Forks

eval-view
24
just-eval
7

Open issues

eval-view
2
just-eval
2

Language

eval-view
Python
just-eval
Python

Adopt for

eval-view
Eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions through diffing tool and
just-eval
just-eval provides a straightforward GPT-based framework for evaluating LLMs across multiple dimensions with clear outputs.

Persona

eval-view
-
just-eval
-

Runtime

eval-view
-
just-eval
-

License

eval-view
Apache-2.0
just-eval
MIT

Last pushed

eval-view
Sep 5, 2026
just-eval
Jan 29, 2024

Categories

eval-view
AI Agents, Evaluation & Observability
just-eval
Evaluation & Observability

Trust and health

Maintenance

eval-view
Active (82%)
just-eval
Dormant (18%)

Days since push

eval-view
13d
just-eval
953d

Stars delta

eval-view
+8 (30d)
just-eval
0 (30d)

Open issues delta

eval-view
-1 (30d)
just-eval
0 (30d)

Owner type

eval-view
User
just-eval
Organization

Full report

eval-view
Trust report
just-eval
Trust report

Shared compatibility

  • Python · eval-view: Python runtime · just-eval: Python runtime

Choose eval-view if…

  • License: eval-view is Apache-2.0, just-eval is MIT.
  • Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, ai-agents.
  • Also covers AI Agents.
  • eval-view ships Docker support for self-hosted deployment.
  • When you need to snapshot and diff the behavior of AI agents across multiple platforms, including LangGraph, CrewAI, OpenAI, and Anthropic.

When NOT to use eval-view

  • If you are working exclusively with AI platforms not supported by eval-view, such as those not listed among LangGraph, CrewAI, OpenAI, and Anthropic.
  • When you do not require regression testing or behavior snapshotting for your AI agents, as eval-view is specifically designed for these purposes.
  • If you are looking for a tool that does not involve backend API charges for executing your agent, as eval-view does not skip these charges even with the --no-judge flag.
  • If you need a tool that automatically handles the migration from the OpenAI Assistants API to the Responses API without manual intervention, as eval-view requires following a migration guide for this.
GitHubDetails

Choose just-eval if…

  • License: just-eval is MIT, eval-view is Apache-2.0.
  • Tags unique to just-eval: evaluation, gpt4, llm, llm-eval.
  • When your team requires an in-depth yet interpretable evaluation of LLM performance, focusing on various aspects.

When NOT to use just-eval

  • Avoid if direct integration with competing API services is needed as just-eval focuses solely on OpenAI API key configuration.
  • Not suitable when your project demands a custom evaluation framework that extends beyond simple GPT-based assessments.
GitHubDetails

Explore

eval-view trust report →just-eval trust report →AI Agents category →Evaluation & Observability category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: eval-view 134 · just-eval 90 (synced Sep 20, 2026).

Common questions

What is the difference between eval-view and just-eval?
eval-view: Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI. just-eval: A simple GPT-based evaluation tool for multi-aspect, interpretable assessment of LLMs.. See the comparison table for live GitHub stats and shared categories.
When should I choose eval-view over just-eval?
Choose eval-view over just-eval when License: eval-view is Apache-2.0, just-eval is MIT; Tags unique to eval-view: agent-benchmark, agent-evaluation, agentic-ai, ai-agents; Also covers AI Agents; eval-view ships Docker support for self-hosted deployment; When you need to snapshot and diff the behavior of AI agents across multiple platforms, including LangGraph, CrewAI, OpenAI, and Anthropic.
When should I choose just-eval over eval-view?
Choose just-eval over eval-view when License: just-eval is MIT, eval-view is Apache-2.0; Tags unique to just-eval: evaluation, gpt4, llm, llm-eval; When your team requires an in-depth yet interpretable evaluation of LLM performance, focusing on various aspects.
When should I avoid eval-view?
If you are working exclusively with AI platforms not supported by eval-view, such as those not listed among LangGraph, CrewAI, OpenAI, and Anthropic. When you do not require regression testing or behavior snapshotting for your AI agents, as eval-view is specifically designed for these purposes. If you are looking for a tool that does not involve backend API charges for executing your agent, as eval-view does not skip these charges even with the --no-judge flag. If you need a tool that automatically handles the migration from the OpenAI Assistants API to the Responses API without manual intervention, as eval-view requires following a migration guide for this.
When should I avoid just-eval?
Avoid if direct integration with competing API services is needed as just-eval focuses solely on OpenAI API key configuration. Not suitable when your project demands a custom evaluation framework that extends beyond simple GPT-based assessments.
Is eval-view or just-eval more popular on GitHub?
eval-view has more GitHub stars (134 vs 90). Stars measure visibility, not whether either tool fits your constraints.
Are eval-view and just-eval open source?
Yes - both are open-source projects on GitHub (eval-view: Apache-2.0, just-eval: MIT).
Where can I find alternatives to eval-view or just-eval?
GraphCanon lists graph-backed alternatives at eval-view alternatives and just-eval alternatives (eval-view markdown twin, just-eval markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, eval-view or just-eval?
eval-view: Active. just-eval: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for eval-view and just-eval?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: eval-view trust report; just-eval trust report.

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